Statistical method for assessing autonomic balance

ABSTRACT

A computationally efficient method for assessing a subject&#39;s autonomic balance by measurement of heart rate variability is disclosed which is particularly suitable for implementation by an implantable medical device. Statistical surrogates are used to represent frequency components of an RR time series. A ratio of the low frequency component to the high frequency component may then be estimated to assess the subject&#39;s autonomic balance.

CROSS REFERNCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. patent application Ser. No. 10/436,876, filed on May 12, 2003, the specification of which is incorporated herein by reference.

FIELD OF THE INVENTION

This invention pertains to cardiac rhythm management devices such as pacemakers and implantable monitoring devices.

BACKGROUND

Heart rate variability (HRV) refers to the changes in the length of time between consecutive heart beats during sinus rhythm and is primarily due to the interaction between the sympathetic and parasympathetic arms of the autonomic nervous system. Measurement and analysis of heart rate variability is thus a useful and non-invasive tool for assessing the status of the autonomic nervous system.

A heart beat is usually measured as the time from the peak of one R wave to the peak of the next, referred to as an RR interval. The variability of normal RR intervals (i.e., during sinus rhythm) can be determined and analyzed in several different ways in either the time domain or the frequency domain. Time domain measurements involve the computation of a statistic based upon the individual RR intervals making up an RR time series such as the standard deviation of the RR intervals in the series. Frequency domain analysis, on the other hand, employs methods such as the Fast Fourier Transform (FFT) or autoregressive analysis to analyze the frequency spectrum of the variability in the RR intervals. This latter type of analysis has proven to be particularly valuable in assessing the relative activities of the sympathetic and parasympathetic nervous systems in a subject. Such assessment of the state of autonomic balance would be a useful function for implantable cardiac rhythm management devices such as pacemakers and implantable cardioverter/defibrillators to perform as it could be used to modify the manner in which therapy is delivered by the device or to predict the occurrence of arrhythmias. Frequency domain analysis of heart rate variability, however, requires computational and data storage capabilities that may not be practical in present-day implantable devices.

SUMMARY

The present invention is a method for assessing the autonomic balance of a subject by estimating the ratio of certain frequency components in an RR time series based upon statistics computed from the RR intervals making up the series. The method is especially suitable for use by an implantable device since the required statistics may be computed without the processing overhead and data storage capability associated with frequency domain analysis. The statistics used to estimate the frequency component ratio may be computed by cumulatively summing particular functions of RR interval measurements or by a histogram technique in which the relative frequencies of RR interval values or functions thereof are calculated from running counts of the RR interval measurements whose values are within specified ranges.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary cardiac rhythm management device for practicing the present invention.

FIGS. 2A through 2D illustrate an exemplary spectrum of an RR time series and the frequency responses represented by statistical surrogates.

DETAILED DESCRIPTION

As noted above, heart rate variability refers to the variability of the time intervals between successive heart beats during a sinus rhythm. Spectral analysis of heart rate variability involves decomposing a signal representing successive beat-to-beat intervals into separate components representing the amplitude of the signal at different oscillation frequencies. It has been found that the amount of signal power in a low frequency (LF) band ranging from 0.04 to 0.15 Hz is influenced by the levels of activity of both the sympathetic and parasympathetic nervous systems, while the amount of signal power in a high frequency band (HF) ranging from 0.15 to 0.40 Hz is primarily a function of parasympathetic activity. The ratio of the signal powers, designated as the LF/HF ratio, is thus a good indicator of the state of autonomic balance, with a high LF/HF ratio indicating increased sympathetic activity. If an implantable medical device could monitor the LF/HF ratio, the device could log a clinically significant event when the ratio exceeds a specified threshold value, as well as possibly automatically altering its mode of operation (e.g., initiating different therapies or performing more computationally intensive data analysis for arrhythmia prediction).

A series of RR interval values can be regarded as a discrete signal indexed by heartbeat such that each value of the signal represents an RR interval for a particular heartbeat. In order to properly analyze the frequency content of heart rate variability, however, the RR time series should be resampled at a specified sampling frequency in order to equalize the time intervals between interval values and thus convert the time series into a discrete time signal, where the sampling frequency is selected to meet the Nyquist criterion with respect to the frequencies of interest. Spectral analysis of such an RR interval signal can then be performed directly in the frequency domain using discrete Fourier transform or autoregression techniques in order to compute the LF/HF ratio. A time-domain technique for determining the high and low frequency components of the signal could also be used in which the RR interval signal is input to low band and high band digital filters and signals proportional to the power of the RR interval signal in each of the low frequency and high frequency bands are derived so that the LF/HF ratio may be computed. Both frequency domain and time domain analysis performed in this manner are computationally intensive, however, and require the storage of large amounts of RR interval data. Such methods may therefore not be practical in a typical implantable medical device which is a small battery-powered device with limited processing power. As described below, statistical techniques that do not involve such processing overhead may be used to generate surrogate parameters from which the LF/HF ratio may be calculated.

1. Exemplary Implantable Device Description

Cardiac rhythm management devices are implantable devices that provide electrical stimulation to selected chambers of the heart in order to treat disorders of cardiac rhythm. A pacemaker, for example, is a cardiac rhythm management device that paces the heart with timed pacing pulses. The most common condition for which pacemakers are used is in the treatment of bradycardia, where the ventricular rate is too slow. Cardiac rhythm management devices may also treat tachyarrhythmias, where the heart rate is too fast, by anti-tachycardia pacing and/or delivery of defibrillation shocks. Such devices are usually implanted subcutaneously on the patient's chest and connected to electrodes by leads threaded through the vessels of the upper venous system into the heart. An electrode can be incorporated into a sensing channel that generates an electrogram signal representing cardiac electrical activity at the electrode site and/or incorporated into a pacing or shocking channel for delivering pacing or shock pulses to the site.

A block diagram of an implantable cardiac rhythm management device is shown in FIG. 1. The controller of the device is made up of a microprocessor 10 communicating with a memory 12 via a bidirectional data bus, where the memory 12 typically comprises a ROM (read-only memory) for program storage and a RAM (random-access memory) for data storage. The controller could be implemented by other types of logic circuitry (e.g., discrete components or programmable logic arrays) using a state machine type of design, but a microprocessor-based system is preferable. As used herein, the programming of a controller should be taken to refer to either discrete logic circuitry configured to perform particular functions or to executable code stored in memory or other storage medium. The controller is capable of operating the device so as to deliver a number of different therapies in response to detected cardiac activity. A telemetry interface 80 is also provided for enabling the controller to communicate with an external programmer.

The embodiment shown in FIG. 1 has two sensing/pacing channels, where a pacing channel is made up of a pulse generator connected to an electrode while a sensing channel is made up of the sense amplifier connected to an electrode. A MOS switch matrix 70 controlled by the microprocessor is used to switch the electrodes from the input of a sense amplifier to the output of a pulse generator. The switch matrix 70 also allows the sensing and pacing channels to be configured by the controller with different combinations of the available electrodes. The channels may be configured as either atrial or ventricular channels. In an example configuration, an atrial sensing/pacing channel includes ring electrode 43 a and tip electrode 43 b of bipolar lead 43 c, sense amplifier 41, pulse generator 42, and a channel interface 40. A ventricular sensing/pacing channel includes ring electrode 33 a and tip electrode 33 b of bipolar lead 33 c, sense amplifier 31, pulse generator 32, and a channel interface 30. The channel interfaces communicate bi-directionally with a port of microprocessor 10 and may include analog-to-digital converters for digitizing sensing signal inputs from the sensing amplifiers, registers that can be written to for adjusting the gain and threshold values of the sensing amplifiers, and registers for controlling the output of pacing pulses and/or changing the pacing pulse amplitude. A shock pulse generator 20 is also interfaced to the controller for delivering defibrillation shocks through electrodes selected by the switch matrix. In the illustrated embodiment, the device is equipped with bipolar leads that include two electrodes which are used for outputting a pacing pulse and/or sensing intrinsic activity. Other embodiments may employ unipolar leads with single electrodes for sensing and pacing. The switch matrix 70 may configure a channel for unipolar sensing or pacing by referencing an electrode of a unipolar or bipolar lead with the device housing or can 60.

The controller 10 controls the overall operation of the device in accordance with programmed instructions stored in memory. The controller 10 interprets electrogram signals from the sensing channels in order to control the delivery of paces in accordance with a pacing mode and/or deliver shock therapy in response to detection of a tachyarrhythmia such as ventricular fibrillation. The sensing circuitry of the device generates atrial and ventricular electrogram signals from the voltages sensed by the electrodes of a particular channel. An electrogram is analogous to a surface ECG and indicates the time course and amplitude of cardiac depolarization that occurs during either an intrinsic or paced beat. When an electrogram signal in an atrial or sensing channel exceeds a specified threshold, the controller detects an atrial or ventricular sense, respectively, which pacing algorithms may employ to trigger or inhibit pacing and from which heart rates may be derived by measuring the intervals between senses.

2. Statistical Assessment of Autonomic Balance

A cardiac rhythm management device such as illustrated in FIG. 1 can be programmed to determine heart rate variability by analyzing data received from its ventricular sensing channels. The intervals between successive ventricular senses, referred to as RR intervals, can be measured for a specified period of time or a specified number of beats and their variability analyzed. A typical RR time series, for example, would be made up of RR intervals over 24 hours or other long-term period. In order to derive a signal representing heart rate variability during a sinus rhythm, ectopic ventricular beats (i.e., premature ventricular contractions or PVCs) can be detected by monitoring whether a P wave precedes each R wave, with the RR intervals before and after the PVC changed to an interpolated or otherwise filtered value. An RR interval signal collected in this manner could be spectrally analyzed by the device in order to determine the frequency content in the LF and HF bands by either transforming the signal into the frequency domain or by decomposing the signal with bandpass filters. Both of these approaches, however, require extensive calculations and the storage of large amounts of data. A less computationally intensive way of spectrally analyzing an RR time series is to employ certain statistical parameters as surrogates for the actual specific frequency components.

FIG. 2A illustrates a log frequency plot of the spectrum an example RR time series showing a very low frequency component VLF between DC (i.e., no variability in the RR intervals) and 0.04 Hz, a low frequency component LF between 0.04 Hz and 0.15 Hz, and a high frequency component HF between 0.15 and 0.40 Hz. FIGS. 2B through 2D illustrate how the frequency components of an RR time series would be reflected by different statistical parameters. The rMSSD statistic is defined as the square root of the mean of the squared successive differences of an RR time series: rMSSD=E{(RR _(i) −RR _(i−1))²}^(0.5) where E is the expectation or mean value operator, and RR_(i) refers to the ith RR interval in the series. The square root step in the calculation can be omitted to give the [rMSSD]² parameter. By averaging the square of the successive interval-to-interval difference values in the RR time series, the rMSSD or [rMSSD]² statistic maximally reflects variations in the RR intervals that occur with each successive interval and progressively attenuates variations in the RR intervals that occur at lower frequencies. The frequency response represented by the rMSSD statistic shown in FIG. 2B is therefore greatest at the maximum frequency that can be represented in the time series (i.e., approximately one-half of the average heart rate, similar to the Nyquist frequency in a time series with regular intervals) and then decreases linearly with decreasing frequency so that lower frequency variability in the RR time series is not represented. Computation of the rMSSD or [rMSSD]² statistic thus captures a frequency range of heart rate variability which is similar to the HF band illustrated in FIG. 2A.

The SD₇ statistic is defined as the standard deviation of the mean values of all successive 7-second segments in the RR time series, and [SD₇]² is the square of that standard deviation or variance. By computing mean values of the RR intervals over 7-second segments, the [SD₇]² statistic averages out the variations in the RR intervals that occur over time intervals equal to or smaller than 7 seconds (i.e., variations at frequencies higher than approximately 0.15 Hz), with the variations in the RR intervals occurring over longer intervals then being reflected by computation of the variance of those mean values. FIG. 2C illustrates the frequency response of the [SD₇]² statistic which shows capture of those frequency components of the RR interval signal from some frequency above DC (since a variance calculation eliminates the DC component of a signal) to approximately 1/7 or 0.15 Hz (i.e., a frequency with a period equal to the length of the 7-second segment). The SD₂₅ statistic is similarly defined as the standard deviation of the mean values of all successive 25-second segments in the RR time series, where [SD₂₅]² is the square of that standard deviation or variance. FIG. 2D illustrates the frequency response of the [SD₂₅]² statistic which shows capture of those frequency components of the RR interval signal from some frequency above DC to approximately 1/25 or 0.04 Hz (i.e., a frequency with a period equal to the length of the 25-second segment). If the [SD₂₅]² statistic is subtracted from the [SD₇]² statistic, the resulting parameter captures a frequency range of heart rate variability which is similar to the LF band illustrated in FIG. 2A.

The rMSSD, [SD₂₅]², and [SD₇]² statistics may thus serve as surrogates for the frequency components of an RR time series. An estimate of the LF/HF ratio may then be computed as: Estimated LF/HF=K{[SD ₇]² −[SD ₂₅]² }/[rMSSD] ² where K is a constant. A linear regression analysis may be performed in which the estimated values are correlated with the actual spectrum of an RR time series to derive the value of K. Alternatively, the estimated LF/HF ratio may be compared with appropriately scaled threshold values in order to assess the autonomic balance of a subject, which eliminates the need for K in the calculation. As described in more detail below, estimating the LF/HF ratio in this manner is much less computationally intensive than direct spectral analysis since the statistical surrogates for frequency content can be computed by maintaining cumulative sums or counts of functions of the measured RR intervals.

A description of an embodiment of the method for estimating an LF/HF ratio and assessing a subject's autonomic balance using statistical surrogates which may be implemented by an implantable device is as follows. The device measures RR intervals between each pair of successive ventricular senses over a predetermined long-term period (e.g., 24 hours) to result in RR interval measurements of an RR time series RR₁ through RR_(N) where N is the total number of RR interval measurements during the predetermined long-term period. Mean values M_(x1) through M_(xL) of successive x-second segments of the RR time series are computed, where x is a predetermined number (e.g., 7) and L is the total number of such x-second segments in the RR time series. Mean values M_(y1) through M_(yK) of successive y-second segments of the RR time series also computed, where y is a predetermined number greater than x (e.g., 25) and K is the total number of such y-second segments in the RR time series. Variances of the mean values M_(x1) through M_(xL) and of the mean values M_(y1) through M_(yK) are next computed, referred to as [SD_(x)]² and [SD_(y)]²; respectively. A mean value of squared successive differences between the RR intervals in the RR time series is next computed to give the [rMSSD]² parameter. The ratio of the low frequency content in the RR time series between approximately 1/x Hz and 1/y Hz and higher frequency content in the RR time series, referred to as LF/HF, can then be calculated as: LF/HF=K([SD _(x)]² −[SD _(y)]²)/[rMSSD] ² where K is a defined constant and LF/HF is taken to be reflective of the subject's autonomic balance with appropriate selection of the x and y values.

In order to compute the statistics, a histogram technique may be employed where cumulative counts of particular functions of the RR interval measurements are maintained. For example, to compute the [SD_(x)]² statistic, an integral number T of interval bins A₁ through A_(T) representing interval values I₁ through I_(T) are defined. Each computed mean value M_(x1) through M_(xL) of the successive x-second segments of the RR time series is then assigned to a corresponding one of the interval bins A₁ through A_(T). That is, each computed mean value is assigned to the bin representing an interval value to which it is closest to thereby maintain a cumulative count of the values taken on by the computed mean values. The number of computed mean values assigned to each interval bin A₁ through A_(T) is counted and divided by L to derive a relative frequency FX_(i) for each interval value I_(i). [SD_(x)]² may then be computed as: [SD _(x)]²=Σ(I _(i))² FX _(i)−(Σ(I _(i))FX _(i))² where the summations are carried out from i=1 to T.

The [SD_(y]) ² statistic may be similarly computed. An integral number S of interval bins B₁ through B_(S) representing interval values I₁ through I_(S) are defined. Each computed mean value M_(y1) through M_(yK) of the successive x-second segments of the RR time series is then assigned to a corresponding one of the interval bins B₁ through B_(S). The number of computed mean values assigned to each interval bin B₁ through B_(S) is counted and divided by K to derive a relative frequency FY_(i) for each interval value I_(i). The [SD_(y)]² may then be computed as: [SD _(y)]²=Σ(I _(i))² FY _(i)−(Σ(I _(i))FY _(i))² where the summations are carried out from i=1 to S.

A histogram technique may also be used to calculate the [rMSSD]² statistic. An integral number W of RR interval difference bins C₁ through C_(W) representing RR interval difference values D₁ through D_(W) are defined. An interval difference between each pair of RR intervals in the RR time series is computed as (RR₂−RR₁) through (RR_(N)−RR_(N−1)). Each of the computed interval differences between RR intervals in the RR time series is then assigned to a corresponding one of the RR interval difference bins C₁ through C_(W), and the number of computed interval differences assigned to each RR interval difference bin C₁ through C_(W) is divided by N−1 to derive a relative frequency FD_(i) for each interval difference D_(i). The [rMSSD]² is then computed as: [rMSSD] ²=Σ(D _(i))² FD _(i) where the summation is carried out from i=1 to W.

Alternatively, the statistics may be computed by cumulatively summing functions of the RR interval differences. For example, the [SD_(x)]² statistic may be computed by cumulatively summing each computed mean value M_(xi) and cumulatively summing each computed mean value M_(xi) squared so that [SD_(x)]² may be computed as: [SD _(x)]²=(1/L)Σ(M _(xi))²−((1/L)ΣM _(xi))² where the summations are carried out from i=1 to L by the cumulative summing operations. Similarly, the [SD_(y)]² statistic may be computed by cumulatively summing each computed mean value M_(yi) and cumulatively summing each computed mean value M_(yi) squared so that [SD_(y)]² may be computed as: [SD _(y)]²=(1/L)Σ(M _(yi))²−((1/L)ΣM _(yi))² where the summations are carried out from i=1 to K by the cumulative summing operations. The [rMSSD]² statistic may be computed by cumulatively summing each computed interval difference value (RR_(i+1)−RR_(i)) squared and then computing [rMSSD]² as: [rMSSD] ²=(1/(N−1))Σ(RR _(i+1) −RR _(i))² where the summation is carried out from i=1 to N−1 by the cumulative summing operation.

As noted earlier, investigators have generally found that the optimum frequency ranges for computation of the LF/HF ratio in order to assess autonomic balance is with an LF band between 0.04 Hz and 0.15 Hz and with an HF band between 0.15 Hz and 0.40 Hz. This would correspond to x and y values in the above description of 7 and 25, respectively. Different LF and HF frequency ranges and/or different x and y values, however, may be found to more optimally assess autonomic balance in a particular patient. Also, a standard long-term period over which to define an RR time series is 24 hours, but a different long-term period may be more appropriate in certain circumstances. In another embodiment of the method, a moving average of estimated LF/HF ratios over successive long-term time periods may be computed.

Although the invention has been described in conjunction with the foregoing specific embodiments, many alternatives, variations, and modifications will be apparent to those of ordinary skill in the art. Other such alternatives, variations, and modifications are intended to fall within the scope of the following appended claims. 

1. A method, comprising: measuring time intervals between successive heart beats, referred to as RR intervals, over a predetermined long-term period, where N is the total number of RR interval measurements during the predetermined long-term period and the measured RR intervals RR₁ through RR_(N) are referred to as an RR time series; computing mean values M_(x1) through M_(xL) of successive x-second segments of the RR time series, where x is a predetermined number and L is the total number of such x-second segments in the RR time series, and computing a variance of the mean values M_(x1) through M_(xL), referred to as [SD_(x)]²; computing mean values M_(y1) through M_(yK) of successive y-second segments of the RR time series, where y is a predetermined number greater than x and K is the total number of such y-second segments in the RR time series, and computing a variance of the mean values M_(y1) through M_(yK), referred to as [SD_(y)]²; computing a mean value of squared successive differences between the RR intervals in the RR time series, referred to as [rMSSD]²; and, estimating a ratio of the low frequency content in the RR time series between approximately 1/x Hz and 1/y Hz and higher frequency content in the RR time series, referred to as LF/HF, as: LF/HF=K([SD _(x)]² −[SD _(y)]²)/[rMSSD] ² wherein K is a defined constant.
 2. The method of claim 1 further comprising: defining an integral number T of RR interval bins A₁ through A_(T) representing RR interval values I₁ through I_(T); assigning each computed mean value M_(x1) through M_(xL) of the successive x-second segments of the RR time series to a corresponding one of the RR interval bins A₁ through A_(T); counting the number of computed mean values assigned to each RR interval bin A₁ through A_(T) and dividing each such number by L to derive a relative frequency FX_(i) for each interval value I_(i); and, computing [SD_(x)]² as [SD _(x)]²=Σ(I _(i))² FX _(i)−(Σ(I _(i))FX _(i))² where the summations are carried out from i=1 to T.
 3. The method of claim 1 further comprising: defining an integral number S of RR interval bins B₁ through B_(S) representing RR interval values I₁ through I_(S); assigning each computed mean value M_(y1) through M_(yK) of the successive y-second segments of the RR time series to a corresponding one of the RR interval bins B₁ through B_(S); counting the number of computed mean values assigned to each RR interval bin B₁ through B_(S) and dividing each such number by K to derive a relative frequency FY_(i) for each interval value I_(i); and, computing [SD_(y)]² as [SD _(y)]²=Σ(I _(i))² FY _(i)−(Σ(I _(i))FY _(i))² where the summations are carried out from i=1 to S.
 4. The method of claim 1 further comprising: defining an integral number W of RR interval difference bins C₁ through C_(W) representing RR interval difference values D₁ through D_(W); computing an interval difference between each pair of RR intervals in the RR time series as (RR₂−RR₁) through (RR_(N)−RR_(N−1)); assigning each of the computed interval differences between RR intervals in the RR time series to a corresponding one of the RR interval difference bins C₁ through C_(W); counting the number of computed interval differences assigned to each RR interval difference bin C₁ through C_(W) and dividing each such number by N−1 to derive a relative frequency FD_(i) for each interval difference D_(i); and, computing [rMSSD]² as [rMSSD] ²=Σ(D _(i))² FD _(i) where the summation is carried out from i=1 to W.
 5. The method of claim 1 further comprising: cumulatively summing each computed mean value M_(xi); cumulatively summing each computed mean value M_(xi) squared; computing [SD_(x)]² as: [SD _(x)]²=(1/L)Σ(M _(xi))²−((1/L)ΣM _(xi))² where the summations are carried out from i=1 to L by the cumulative summing operations.
 6. The method of claim 1 further comprising: cumulatively summing each computed mean value M_(yi); cumulatively summing each computed mean value M_(yi) squared; computing [SD_(y)]² as: [SD _(y)]²=(1/L)Σ(M _(yi))²−((1/L)ΣM _(yi))² where the summations are carried out from i=1 to K by the cumulative summing operations.
 7. The method of claim 1 further comprising: cumulatively summing each computed interval difference value (RR_(i+1)−RR_(i)) squared; computing [rMSSD]² as: [rMSSD] ²=(1/(N−1))Σ(RR _(i+1) −RR _(i))² where the summation is carried out from i=1 to N−1 by the cumulative summing operation.
 8. The method of claim 1 wherein the predetermined long-term period is 24 hours.
 9. The method of claim 1 wherein the predetermined numbers x and y are 7 and 25, respectively, such that the estimated LF/HF represents a ratio of the low frequency content in the RR time series between approximately 0.04 Hz and 0.15 Hz and the high frequency content in the RR time series between approximately 0.15 Hz and 0.4 Hz.
 10. The method of claim 1 further comprising computing a moving average of estimated LF/HF ratios over successive long-term time periods.
 11. A system, comprising: means for measuring time intervals between successive heart beats, referred to as RR intervals, over a predetermined long-term period, where N is the total number of RR interval measurements during the predetermined long-term period and the measured RR intervals RR₁ through RR_(N) are referred to as an RR time series; means for computing mean values M_(x1) through M_(xL) of successive x-second segments of the RR time series, where x is a predetermined number and L is the total number of such x-second segments in the RR time series, and computing a variance of the mean values M_(x1) through M_(xL), referred to as [SD_(x)]²; means for computing mean values M_(y1) through M_(yK) of successive y-second segments of the RR time series, where y is a predetermined number greater than x and K is the total number of such y-second segments in the RR time series, and computing a variance of the mean values M_(y1) through M_(yK), referred to as [SD_(y)]²; means for computing a mean value of squared successive differences between the RR intervals in the RR time series, referred to as [rMSSD]²; and, means for estimating a ratio of the low frequency content in the RR time series between approximately 1/x Hz and 1/y Hz and higher frequency content in the RR time series, referred to as LF/HF, as: LF/HF=K([SD _(x)]² −[SD _(y)]²)/[rMSSD] ² wherein K is a defined constant.
 12. The system of claim 11 further comprising: means for defining an integral number T of RR interval bins A₁ through A_(T) representing RR interval values I₁ through I_(T); means for assigning each computed mean value M_(x1) through M_(xL) of the successive x-second segments of the RR time series to a corresponding one of the RR interval bins A₁ through A_(T); means for counting the number of computed mean values assigned to each RR interval bin A₁ through A_(T) and dividing each such number by L to derive a relative frequency FX_(i) for each interval value I_(i); and, means for computing [SD_(x)]² as [SD _(x)]²=Σ(I _(i))² FX _(i)−(Σ(I _(i))FX _(i))² where the summations are carried out from i=1 to T.
 13. The system of claim 11 further comprising: means for defining an integral number S of RR interval bins B₁ through B_(S) representing RR interval values I₁ through I_(S); means for assigning each computed mean value M_(y1) through M_(yK) of the successive y-second segments of the RR time series to a corresponding one of the RR interval bins B₁ through B_(S); means for counting the number of computed mean values assigned to each RR interval bin B₁ through B_(S) and dividing each such number by K to derive a relative frequency FY_(i) for each interval value I_(i); and, means for computing [SD_(y)]² as [SD _(y)]²=Σ(I _(i))² FY _(i)−(Σ(I _(i))FY _(i))² where the summations are carried out from i=1 to S.
 14. The system of claim 11 further comprising: means for defining an integral number W of RR interval difference bins C₁ through C_(W) representing RR interval difference values D₁ through D_(W); means for computing an interval difference between each pair of RR intervals in the RR time series as (RR₂−RR₁) through (RR_(N)−RR_(N−1)); means for assigning each of the computed interval differences between RR intervals in the RR time series to a corresponding one of the RR interval difference bins C₁ through C_(W); means for counting the number of computed interval differences assigned to each RR interval difference bin C₁ through C_(W) and dividing each such number by N−1 to derive a relative frequency FD_(i) for each interval difference D_(i); and, means for computing [rMSSD]² as [rMSSD] ²=Σ(D _(i))² FD _(i) where the summation is carried out from i=1 to W.
 15. The system of claim 11 further comprising: means for cumulatively summing each computed mean value M_(xi); means for cumulatively summing each computed mean value M_(xi) squared; means for computing [SD_(x)]² as: [SD _(x)]²=(1/L)Σ(M _(xi))²−((1/L)ΣM _(xi))² where the summations are carried out from i=1 to L by the cumulative summing operations.
 16. The system of claim 11 further comprising: means for cumulatively summing each computed mean value M_(yi); means for cumulatively summing each computed mean value M_(yi) squared; means for computing [SD_(y)]² as: [SD _(y)]²=(1/L)Σ(M _(yi))²−((1/L)ΣM _(yi))² where the summations are carried out from i=1 to K by the cumulative summing operations.
 17. The system of claim 11 further comprising: means for cumulatively summing each computed interval difference value (RR_(i+1)−RR_(i)) squared; means for computing [rMSSD]² as: [rMSSD] ²=(1/(N−1))Σ(RR _(i+1) −RR _(i))² where the summation is carried out from i=1 to N−1 by the cumulative summing operation.
 18. The system of claim 11 wherein the predetermined long-term period is 24 hours.
 19. The system of claim 11 wherein the predetermined numbers x and y are 7 and 25, respectively, such that the estimated LF/HF represents a ratio of the low frequency content in the RR time series between approximately 0.04 Hz and 0.15 Hz and the high frequency content in the RR time series between approximately 0.15 Hz and 0.4 Hz.
 20. The system of claim 11 further comprising means for computing a moving average of estimated LF/HF ratios over successive long-term time periods. 